2025/02/13 by Austin Houston, Sumner B. Harris, Houston, Austin C. +11 · 1 citation
Materials Science · #Computer Vision and Pattern Recognition (cs.CV) #Diatoms and Algae Research #FOS: Computer and information sciences #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Quasicrystal Structures and Properties
paper · pdf · doi:10.48550/arxiv.2502.09791
openalex publication_date 2025/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, effectively deconvoluting the moire pattern. This enables layer-specific mapping of quantities like strain and dopant distributions, unlike other commonly used segmentation models which struggle with moire-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.